Electric field touchscreen

ABSTRACT

An electric field (e-field) touchscreen is described. A continuous stream of digital signal data that represents e-field signal deviations is received from multiple receive electrodes. The stream of digital signal data is processed using a machine learning model to determine a touch event and a location on a display screen of the touchscreen. The touch event is processed. The e-field touchscreen may determine whether a non-normal event may be occurring causing noise in the digital signal data. If so, the received stream of digital signal data is processed through a low-pass filter and processed through an absolute value average baseline filter. A difference between the filtered data is determined to generate a filtered stream of digital signal data and is processed using the machine learning model determine a touch event and a location on a display screen of the touch event. The touch event is processed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/297,396, filed Mar. 8, 2019, which is hereby incorporated byreference.

FIELD

Embodiments of the invention relate to the field of touchscreens, andmore specifically, to an electric field touchscreen.

BACKGROUND

Touchscreens are commonplace in everyday life. Touchscreens are used inmany devices such as mobile devices, personal computers, point-of-salesystems, automated teller machines, etc. There are a variety oftechnologies to implement touchscreen technology.

One kind of touchscreen is a resistive touchscreen. Resistivetouchscreens are made of several layers where the outer layer, whenpressed down by an object such as a fingertip or stylus, contacts with alower layer. The position of the point of pressure on the screen canthen be determined. Since resistive touchscreens work on pressure, theycan be used by people wearing gloves or using other inanimate objectssuch as a non-capacitive stylus. Resistive touchscreens are typicallymore prone to damage than other types of touchscreens. Resistivetouchscreens require calibration over time (due to wear and otherevents). Resistive touchscreens wear down over time developing deadspots in places of frequent use.

Another type of touchscreen is an infrared touchscreen that uses light(typically emitted by light emitting diodes) to generate a matrix andsensors to identify when the plane of the matrix is broken by an object(such as a finger). The light emitting diodes of a touchscreen will burnout and an infrared touchscreen is impacted by sunlight, dust, dirt, andprecipitation.

Another type of touchscreen is a capacitive touchscreen. Generallycapacitive touchscreens are made with glass that is back-bonded withtransparent conductive materials providing a field of electricallyaddressable matrix of rows and columns for detection. When the screen istouched with a conductive/capacitive object (such as a finger), thereresults a measurable change in capacitance in the screen's field that ismeasured as a change in capacitance. The proximity, reach, or depth offield for capacitive sensor technologies is limited to no more than 4 mm(differential mode). Moreover, the sensor field must be bonded andmounted behind 3 mm (e.g., outside kiosk, ATM, etc.) of reinforcedtouchscreen materials, such as glass and/or polycarbonate. Due thistransparent mount thickness the limited proximity range/depth ofcapacitor sensing is comprised to where it cannot support glovedfingers, long fingernails, and whenever ice and snow builds up on thedisplay screen). Most mobile device touchscreens are capacitivetouchscreens because they must be thin materials.

Both resistive and capacitive touchscreens are subject to vandalismsince the screen is directly exposed to the user and the sensor fieldmust have intimate contact (direct exposure) at the user interface.

The type of touchscreen technology that is used on a device depends inlarge part on the purpose of that device. What is appropriate for asmartphone may not be appropriate for a device that is expected to beused in the elements, such as an automated teller machine, a gas stationpump, or an electric vehicle charging station. For instance,touchscreens that are bonded to glass can be easily cracked or damage ifthey are hit by hail, a rock kicked off the ground by a car tire, orother wind-strewn sticks and other materials. Also, the bonding utilizedin attaching touchscreens to glass does not handle (e.g., delaminate anddiscolor) large changes in temperature or direct UV exposure over a longperiod of time well. As touchscreen size grows, the amount of pressureto cause glass to crack lessens, thereby making large touchscreens madeof glass unlikely to last long without damage.

Electric field (E-Field) technology has been used to support3-dimensional (X, Y, & Z) gesture detection and motion tracking. AnE-field is generated by electrical charges and spread along a surfacecarrying the charge. The E-field becomes distorted when an object, suchas a finger or a hand, enters the E-field area, and the object'sposition relative to the sensing area can be determined. The signals ofe-field technology may be impacted by precipitation including rain.

Although E-field technology can be used to support gesture detection,typical E-field technology does not work well for touchscreen operation.Conventional E-field technology registers gestures several feet from thereceive electrodes. Typical E-field technology does not detect the pointof contact (Z-direction) on the screen but rather detects the approachof the object on the electric field being emitted. This distortion mayinclude, for example, the user's entire finger and a portion of theirhand. Since users use touchscreens in different ways (e.g., some use athumb or pinky, some extend their finger up keeping their wrist below,some hold their hand sideways), simple algorithms to detect an XYlocation are not accurate because they do not account for differentusage patterns. Further, hand sizes are different, so the size of impactvaries greatly.

SUMMARY

An electric field touchscreen is described. The electric field (e-field)touchscreen includes a set of one or more transmit electrodes that areconfigured to generate an e-field, and a plurality of receive electrodesthat are each configured to sense an e-field variance. A controllercontinuously receives signal data from the plurality of receiveelectrodes and outputs a stream of digital signal data that representse-field signal deviations. An application processor receives the streamof digital signal data and process the stream of digital signal datausing a machine learning model to determine a touch event and locationon a display of the touchscreen. The touch event is then processed.

In an embodiment, the application processor determines whether anon-normal event may be occurring that causes noise in the digitalsignal data. For instance, signal strength may drop below a thresholdthat may indicate that a non-normal event may be occurring (e.g., heavyrain or other precipitation) that could lead to a false touch eventoccurring and/or degradation or loss of actual touch events. If thesignal strength drops below the threshold, further filtering of the dataoccurs. Baseline calibration for the stream of digital signal data isdisabled, and the stream of digital signal data is passed through alow-pass filter to generate a first filtered stream of digital signaldata and passed through an absolute value average baseline filter. Adifference between the first filtered stream of digital signal dataabsolute value average is made to generate a second filtered stream ofdigital signal data. The second filtered stream of digital signal datais processed using the machine learning model to determine a touch eventand a location on the display of the touchscreen. The touch event isthen processed.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may best be understood by referring to the followingdescription and accompanying drawings that are used to illustrateembodiments of the invention. In the drawings:

FIG. 1 illustrates a system for an e-field touchscreen according to anembodiment.

FIG. 2 shows an example of the placement of receive electrodes inrelation with the screen of the display according to an embodiment.

FIG. 3 shows a cross section of the touchscreen assembly according to anembodiment.

FIG. 4 is an exploded view of the touchscreen assembly according to anembodiment.

FIG. 5 is a flow diagram that illustrates exemplary operations fordetermining touch events implemented on an e-field touchscreen accordingto an embodiment.

DESCRIPTION OF EMBODIMENTS

An electric field (e-field) touchscreen is described. The e-fieldtouchscreen includes multiple e-field receive electrodes located towardsthe edge of the display. For instance, if the form of the touchscreen isrectangular, there may be one e-field receive electrodes (e.g., anantenna) located along each edge of the display. The display may becovered with a polycarbonate material. A machine learning (ML) algorithmmay be used to translate e-field data to touch position coordinates(e.g., an X and Y coordinate and whether a touch is registered). Thetouch data is provided to the application for touch enabled processing.The e-field touchscreen may detect certain conditions that could lead toa false touch event and/or degradation or loss of actual touch events,and mitigate such conditions.

FIG. 1 illustrates a system for an e-field touchscreen according to anembodiment. The e-field controller 120 includes an analog front end thatinterfaces with the e-field receive electrodes 110 and the e-fieldtransmitter 115. For instance, the e-field controller 120 provides atransmit signal to the e-field transmitter 115 to generate an e-fieldand receives the analog signals from the e-field receive electrodes 110that sense the e-field variance. The e-field transmitter 115 may includea transmit electrode (that may be located on a layer behind the layer ofthe receive electrodes) and a conductive side of an indium oxide (ITO)material over the entire display region that is electrically bonded tothe transmit electrode. The transmit electrode may be a non-transparentcopper ring around the perimeter of the display, and the ITO material istransparent over the entire display region. The transmit electrode toITO connection improves the overall e-field transmitter and provides amore uniform e-field. This improves the accuracy at the center of thedisplay where touch proximity accuracy can be compromised.

The number, shape, and/or placement of the e-field receive electrodesmay depend on the form factor of the display and/or the expected use ofthe touchscreen. For instance, in cases of a rectangular display wheretouch can be expected anywhere on the display, there may be four receiveelectrodes that are located around the edges of the displayrespectively. As another example, if touch was expected only in aspecific part of the display (e.g., on the X axis), there may be lessthan four receive electrodes (e.g., two receive electrodes at theleft/right of the display). As another example, if the display iscircular shaped, the receive electrodes may be curved along differentportions of the edge of the display.

In a specific embodiment, the e-field receive electrodes 110 includesfour receive electrodes (e.g., antennas) that are located around theedge of the display of the touchscreen (e.g., north, east, south, andwest), and may be located on a different layer than the display itselfor embedded on the edges of the display. The e-field receive electrodes110 detect e-field variations at different positions relative to thedisplay that are used when measuring the origin of the electric fielddistortion from the various signals.

FIG. 2 shows an example of the placement of the electrodes of thee-field touchscreen according to an embodiment. The shape of the displayscreen in FIG. 2 is rectangular. As shown in FIG. 2 , there are fourreceive electrodes 210A-210D that are located around the edge of thedisplay screen. The receive electrodes 210A-210D may be antennas and maybe made from copper. The receive electrode 210A may be referred to asthe north electrode, the receive electrode 210B may be referred to asthe east electrode, the receive electrode 210C may be referred as thesouth electrode, and the receive electrode 210D may be referred to asthe west electrode. The transmit electrode 215A (e.g., a copper ring) islocated around the edge of the display, and a conductive side of an ITOmaterial 215B that is electrically bonded to the transmit electrode 215(that together generate the e-field) is located above the LCD displayscreen and below the protective shield. The generated e-field is alongthe entire region of the display. The transmit electrode 215A andconductive ITO material 215B are located in a layer behind the receiveelectrodes 210A-210D. The shape and size of the receive electrodes maybe different than shown in FIG. 2 . For instance, the receive electrodesmay not extend to the edge of each side of the display screen. Asanother example, the receive electrodes 210 may be the same sizerelative to each other or may be a size relative to the size/ratio ofthe screen.

FIG. 3 shows a cross section of the touchscreen assembly according to anembodiment. The liquid crystal display (LCD) 305 is at the bottom layerof the touchscreen assembly. An insulator 308 separates the LCD 305 andthe transmit electrode 310 (that may include a copper ring andconductive ITO material). The insulator 308 may be anon-conductor/insulator material of 5 mm thickness, for example. Theinsulator 312 separates the receive electrodes 315 and the transmitelectrode 310. The insulator 312 may be a non-conductor/insulatormaterial of at least 1.35 mm thick. At the top of the touchscreenassembly layer is the protective shield 320 (e.g., a polycarbonatematerial).

FIG. 4 is an exploded view of the touchscreen assembly according to anembodiment. The part 410 is a printed circuit board combining outward(user facing) e-field receive electrodes 110 on the top side, and thee-field transmitter 115 on the bottom side (a top to bottom insulationdistance is at least 1.3 mm, for example). The part 410 is attached tothe part 415 which is a transparent conducting thin oxide material(e.g., an indium oxide (ITO) material). The part 420 provides theinsulator separation between the LCD display 425.

Referring back to FIG. 1 , the e-field controller 120 processes the rawanalog signals from the e-field receive electrodes 110 digitally in asignal processing unit (SPU) to generate signal data for the measuredelectric fields that represent signal strength. The SPU may performdigital signal processing. In an embodiment, the e-field controller 120performs a calibration process on the incoming data to determine thebaseline of the data (what the data is expected without distortion ofthe e-field). The calibration process may be performed continually. Inanother embodiment, the calibration process is performed by theapplication processor 130 instead of the e-field controller 120. Forinstance, the baseline calibration 158 may be performed in an embodimentwhen the e-field controller 120 does not provide a calibrated baselineof the data. In an embodiment, the e-field controller 120 generatessignal data that is signal deviation values that may be receiver signalsensitivity amplified by receiver gain.

The e-field controller 120 outputs the digital signal data to beprocessed by the application processor 130. The application processor130 processes the digital signal data to determine when a touch eventoccurs on the display and the location of the touch event (e.g., X and Ycoordinates). Certain non-normal events can cause noise in the datawhere it can be difficult to discriminate touch data from noise. Forinstance, precipitation (e.g., heavy rain) increases the peak of thetouch signal level while also filling in the signal troughs where touchand no-touch is discriminated. In an embodiment, the applicationprocessor 130 applies noise specific adaptive filters to discriminatetouch data from noise data. In such an embodiment, the applicationprocessor 130 executes the filter module 160 to determine if filteringthe data is needed to address non-normal events that could lead to falsetouch events being registered and/or degradation or loss of actual touchevents.

The filter module 160 includes the value threshold detection 162 todetect if the digital signal data has dropped below a threshold thatindicates a possibility that a non-normal event is occurring (e.g.,rain, environmental events, etc.) that could lead to a false touch beingregistered and/or degradation or loss of actual touch events. Forinstance, rain or other precipitation may be causing the signal valuesto significantly drop and cause a false touch event to be registeredand/or degrade or lose signal values of actual touch events. The valueof the threshold may be determined through empirical analysis. If thedigital signal data is above the threshold, then the applicationprocessor 130 operates in “normal” mode and a touch calculation module140 that uses a machine learning model 145 to determine if a touch eventoccurs and the location of such a touch event. If the digital signaldata is below the threshold (which is indicative that a non-normal eventis occurring), the application processor 130 operates in a “filteredtouch” mode and performs one or more filters on the digital signal datato mitigate against the non-normal events by removing distortion. Whilein filtered mode, the value threshold detection 162 may be periodicallyexecuted and if the digital signal data exceeds the threshold for apredefined time (e.g., X number of seconds), the application processor130 reverts back to operating in normal mode.

In an embodiment, while in filtered mode, the filter module 160 disablesthe calibration process that determines the baseline of the data. Thisprevents the signals received during filtered mode from becoming a newnormal/baseline. If the e-field controller 120 performs the calibration,the filter module 160 causes a command to be sent to the e-fieldcontroller 120 to stop performing the calibration. If the applicationprocessor 130 performs the calibration, the filter module 160 causes theapplication processor 130 to disable the calibration process.

When in filtered mode, the filter module 160 processes the digitalsignal data through one or more filters 170 to remove distortion in thesignal data. As illustrated in FIG. 1 , the filters include a low-passfilter 172, an absolute value average baseline filter 174, and adifferential filter 176. The filter module 160 may process the digitalsignal data through the low-pass filter 172 and the absolute valueaverage baseline filter 174 concurrently or substantially concurrently.The filtered data from one of the filters is not input into the otherfilters. Instead, the low-pass filter 172 and the absolute value averagebaseline filter 174 independently are applied to the same digital signaldata.

The low-pass filter 172 passes data signals that have a frequency lowerthan a predefined cutoff frequency and attenuates data signals that havea frequency higher than the predefined cutoff frequency. The predefinedcutoff frequency may be determined empirically and may be differentdepending on the environment in which the touchscreen is to be used. Byway of example, the data signals from heavy rain may resemble a sinewave that has periodic oscillation (alternating between high and lowvalues due the rain reflecting signals and absorbing signals). Thelow-pass filter 172 smooths the data signals to slow down the frequencyof change.

The absolute value average baseline filter 174 determines the average ofthe absolute values of the data signals when in filtered mode. Thisbaseline is not the baseline when in normal mode but rather representsthe baseline (of absolute values) of the data signals during thefiltered mode. However, in an embodiment where the e-field controller120 does not provide calibrated e-field data, the absolute value averagebaseline filter 174 may run at all times (even when not in filteredmode) to provide a baseline value for the touch calculation.

The output of the low-pass filter 172 and the output of the absolutevalue average baseline filter 174 are input to the differential filter176. The differential filter 176 subtracts the absolute value averagebaseline as determined by the absolute value average baseline filter 174from the results of the low-pass filter 172 to produce filtered datasignals. The filtered data signals are then provided to the touchcalculation module 140 to determine if a touch event occurs and thelocation of such a touch event.

In an embodiment, the touch calculation module 140 uses the machinelearning model 145 on the signal data (either the unfiltered datasignals when operating in normal mode or the filtered data signals whenoperating in filtered mode) to determine if a touch event occurred andthe location of such a touch event. For instance, the machine learningmodel 145 may be a neural network model that maps the data signals to XYtouch positions. The machine learning model 145 may use a deep neuralnetwork (DNN) regressor estimator to train a regression model to predictthe location (X and Y coordinate) and may use a DNN classifier estimatorto train a classifier model to predict if a touch event has occurred.The machine learning model 145 may have features that correspond withthe e-field receive electrodes 110 (e.g., a north, east, west, and southfeatures). The machine learning model 145 may be implemented by acompiled language (e.g., C or C++) to provide high performance real-timepredictions of a user's input to control the user interface on thedisplay.

The machine learning model 145 may be trained through a diverse group ofusers using the display while the data signals are captured along withadditional sensors (e.g., infrared sensors) providing the targetlocations. The machine learning model can be trained to recognize humantouch at certain x, y positions, allowing the trained model to be ableto discern touches and discard other data. Further, the machine learningmodel may be trained with other interactions that are not touch events,allowing the prediction model to recognize they are not to be recognizedat touch events.

The touch data is then applied to the touch enabled processing 150(e.g., a touch event with the X and Y coordinate). The touch enabledprocessing 150 processes the touch data (e.g., according to itsdefinitions in the UI that is being displayed). For instance, the touchevent may be to control/interact with the user interface beingdisplayed.

Unlike conventional e-field technology that can be used to supportgesture detection, the e-field touchscreen described herein allows foraccurate touchscreen operation. The e-field touchscreen, through use ofthe machine learning model, accurately detects a touch event and itsposition versus detecting the entire impact of the object (e.g., thehand) on the electric field. This allows the e-field touchscreen to beused differently by different users (e.g., different fingers, differenthand placement, different hand sizes, etc.). Also, users wearing glovescan use the e-field touchscreen accurately.

Further, since the display may be protected with a polycarbonatematerial instead of glass, the e-field touchscreen is durable and mayprotect against rocks, hail, baseball bats, etc. Since the touchscreenis not bonded to glass (which conventionally suffers in direct sunlight,heat, or drastic temperature changes), the e-field touchscreen can beused in outdoor conditions for years even when exposed to hightemperatures, low temperatures, snow, ice, and/or direct sunlight. Thisallows the e-field touchscreen to be incorporated in outdoor devices(e.g., an automated teller machine (ATM), an electric vehicle chargingstation, a gas station pump, or outdoor kiosks or other outdoordevices).

Also, unlike conventional touchscreen technologies that fail or poorlyhandle when there is foreign material on the display (e.g., ice, snow,dirt, mud that can physically obstruct the use of the touchscreen), thee-field touchscreen described herein can be accurately used with limitedamounts of foreign material on the display (e.g., ⅛ inch of ice on topof the display) and works accurately when there is snow/ice around theedge or on the display.

FIG. 5 is a flow diagram that illustrates exemplary operations fordetermining touch events implemented on an e-field touchscreen accordingto an embodiment. The operations of FIG. 5 are described with referencethe embodiment shown in FIG. 1 . However, the embodiment shown in FIG. 1can perform operations different than those in FIG. 5 , and theoperations in FIG. 5 can be performed by embodiments different than thatshown in FIG. 1 .

At operation 510, the e-field controller 120 receives electric fieldsignals from each of the e-field receive electrodes 110. These signalsrepresent the variance in the e-field that are sensed by the e-fieldreceive electrodes 110. To generate the e-field, the e-field controller120 causes an e-field transmitter 115 to generate the e-field thatspreads along a surface of the display. The e-field transmitter 115 mayinclude a transmit electrode (that may be located on a layer behind thelayer of the receive electrodes) and a conductive side of an ITOmaterial over the entire display region that is electrically bonded tothe transmit electrode. The transmit electrode may be a non-transparentcopper ring around the perimeter of the display, and the ITO material istransparent over the entire display region. The ITO material may be alow-pressure vapor disposition (a vacuum chamber using electrostaticfield with an energized ingot of material/source) of a lightlyconductive material such as silver oxide. The transmit electrode to ITOconnection improves the overall e-field transmitter and provides a moreuniform e-field. This improves the accuracy at the center of the displaywhere touch proximity accuracy can be compromised. The e-field receiveelectrodes 110 may include four receive electrodes (e.g., antennas) thatare located around the edge of the display of the touchscreen (e.g.,north, east, south, and west), and may be located on a different layerthan the display itself or embedded on the edges of the display.

Next, at operation 515, the e-field controller 120 generates digitalsignal data for the received electric field signals that representssignal strength. The e-field controller 120 may perform a calibrationprocess on the electric field signals to determine the baseline of thedata. The digital signal data that is generated may be signal deviationvalues defined as the receiver signal sensitivity amplified by receivergain. The generated digital signal data is processed by the applicationprocessor 130 to determine if a touch event has occurred and if so thelocation of the touch event.

At operation 520, the application processor 130 determines if the signalstrength (represented in the digital signal data) has dropped below athreshold that indicates that a possibility of a non-normal event isoccurring, and further filtering of the data is warranted. Thisnon-normal event may cause a false touch or other inaccuracy of thee-field touchscreen operation. The non-normal event may occur because ofheavy rain or other environmental factors. For instance, heavyprecipitation may cause the signal strength to significantly drop belowthe threshold and can lead to false touch(es) being registered and/ordegradation or loss of actual touch events. If the signal strength hasdropped below the threshold, then operation 525 is performed. Operations525-540 are performed as part of a filtered mode. If the signal strengthis not below the threshold, then operation 545 is performed.

At operation 525, the application processor 130 disables baselinecalibration of the electric field signals. If the e-field controller 120is performing the baseline calibration, the application processor 130sends a command to the e-field controller 120 to disable the baselinecalibration. If the application processor 130 is performing the baselinecalibration, it ceases to perform the calibration while the signalstrength is below the threshold.

The application processor 130 processes the digital signal data throughone or more filters 170 to remove distortion in the signal data. Forinstance, at operation 530, the application processor 130 processes thedigital signal data using a low-pass filter 172 that passes data signalsthat have a frequency lower than a predefined cutoff frequency andattenuates data signals that have a frequency higher than the predefinedcutoff frequency.

Concurrently (or substantially concurrently) with operation 530, theapplication processor 130 at operation 535 processes the digital signaldata using an absolute value average baseline filter 174. The absolutevalue average baseline filter 174 determines the average of the absolutevalues of the digital signal data. This baseline is not the baselinewhen in normal mode but rather represents the baseline (of absolutevalues) of the data signals during the filtered mode.

Next, at operation 540, the application processor 130 determines thedifference between the results of the low pass filter (operation 530)and the results of the absolute value average baseline filter (operation535) to generate signal data that is filtered. For instance, thedifferential filter 176 takes the result of the absolute value averagebaseline filter (operation 535) and subtracts it from the results of thelow pass filter (operation 530). The filtered signal data is thenprocessed at operation 545.

At operation 545, the application processor 130 processes the signaldata (either the filtered signal data resulting from operation 540 orthe signal data received from the e-field controller 120 resulting fromoperation 515) including using a machine learning model 145 to determinean XY position and a touch event. The machine learning model 145 may bea neural network model that maps the data signals to XY touch positions.The machine learning model 145 may use a deep neural network (DNN)regressor estimator to train a regression model to predict the location(X and Y coordinate) and may use a DNN classifier estimator to train aclassifier model to predict if a touch event has occurred.

Next, at operation 550, the application processor 130 determines if atouch event has occurred based on the processed signal data. If a touchevent has occurred, then at operation 555 the touch event (including theXY position) is processed in an application that uses touch input. Forinstance, the touch event may be to control/interact with the userinterface being displayed. If a touch event has not occurred, then flowmoves back to operation 510.

The e-field touchscreen described can be used for any device that has atouchscreen, including mobile devices (e.g., smartphones, mediaplayers), e-readers, personal computing devices (e.g., laptops, tablets,etc.), wearable devices (e.g., fitness trackers, smart watches, etc.),kiosks, automated teller machines, gas station pumps, electric vehiclecharging stations, point of sale devices, etc.

Although embodiments have described the application processor applyingone or more noise specific adaptive filters to discriminate touch datafrom noise data, in another embodiment the e-field controller 120applies the one or more noise specific adaptive filters to discriminatetouch data from noise data. For instance, the e-field controller 120 mayinclude a filter module like the filter module 160 that determines ifadditional filtering of the data is needed to address non-normal events,may use filters similar to the low-pass filter 172, absolute valueaverage baseline filter 174, and differential filter 176, and maytransmit the filtered e-field data to the application processor 130. Insuch an embodiment, the application processor 130 does not include thefilter module 160 and instead processes the filtered e-field datareceived from the e-field controller 120 using a machine learning modelto determine a touch event and location on the display.

The techniques shown in the figures can be implemented using code anddata stored and executed on one or more electronic devices. Suchelectronic devices store and communicate (internally and/or with otherelectronic devices over a network) code and data using computer-readablemedia, such as non-transitory computer-readable storage media (e.g.,magnetic disks; optical disks; random access memory; read only memory;flash memory devices; phase-change memory) and transitorycomputer-readable communication media (e.g., electrical, optical,acoustical or other form of propagated signals—such as carrier waves,infrared signals, digital signals). In addition, such electronic devicestypically include a set of one or more processors coupled to one or moreother components, such as one or more storage devices (non-transitorymachine-readable storage media), user input/output devices (e.g., akeyboard, a touchscreen, and/or a display), and network connections. Thecoupling of the set of processors and other components is typicallythrough one or more busses and bridges (also termed as bus controllers).Thus, the storage device of a given electronic device typically storescode and/or data for execution on the set of one or more processors ofthat electronic device. Of course, one or more parts of an embodiment ofthe invention may be implemented using different combinations ofsoftware, firmware, and/or hardware.

In the preceding description, numerous specific details were set forthin order to provide a more thorough understanding of the presentinvention. It will be appreciated, however, by one skilled in the artthat the invention may be practiced without such specific details. Inother instances, control structures, gate level circuits and fullsoftware instruction sequences have not been shown in detail in ordernot to obscure the invention. Those of ordinary skill in the art, withthe included descriptions, will be able to implement appropriatefunctionality without undue experimentation.

References in the specification to “one embodiment,” “an embodiment,”“an example embodiment,” etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to affect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

Bracketed text and blocks with dashed borders (e.g., large dashes, smalldashes, dot-dash, and dots) may be used herein to illustrate optionaloperations that add additional features to embodiments of the invention.However, such notation should not be taken to mean that these are theonly options or optional operations, and/or that blocks with solidborders are not optional in certain embodiments of the invention.

In the preceding description and the claims, the terms “coupled” and“connected,” along with their derivatives, may be used. It should beunderstood that these terms are not intended as synonyms for each other.“Coupled” is used to indicate that two or more elements, which may ormay not be in direct physical or electrical contact with each other,co-operate or interact with each other. “Connected” is used to indicatethe establishment of communication between two or more elements that arecoupled with each other.

While the flow diagrams in the figures show a particular order ofoperations performed by certain embodiments of the invention, it shouldbe understood that such order is exemplary (e.g., alternativeembodiments may perform the operations in a different order, combinecertain operations, overlap certain operations, etc.).

While the invention has been described in terms of several embodiments,those skilled in the art will recognize that the invention is notlimited to the embodiments described, can be practiced with modificationand alteration within the spirit and scope of the appended claims. Thedescription is thus to be regarded as illustrative instead of limiting.

What is claimed is:
 1. A method for an electric field (e-field)touchscreen, comprising: continually receiving a stream of digitalsignal data that represents e-field signal deviations detected from eachof a plurality of receive electrodes, wherein a display screen of thee-field touchscreen includes four edges, and wherein the plurality ofreceive electrodes includes at least four electrodes located along thefour edges of the display screen respectively; processing the stream ofdigital signal data using a machine learning model to determine a firsttouch event and a first location on a display screen of the touchscreen;processing the first touch event; determining, from the received streamof digital signal data, that signal strength has dropped below athreshold; and responsive to the determining that signal strength hasdropped below the threshold, performing the following: processing thereceived stream of digital signal data through one or more filters togenerate a first filtered stream of digital signal data, processing thefirst filtered stream of digital signal data using the machine learningmodel to determine a second touch event and a second location on thedisplay screen of the touchscreen, and processing the second touchevent.
 2. The method of claim 1, wherein the machine learning model usesa deep neural network (DNN) regression model to predict locations anduses a DNN classifier model to predict whether touch events haveoccurred.
 3. The method of claim 1, further comprising: responsive tothe determining that signal strength has dropped below the threshold,further performing the following: disabling baseline calibration for thestream of digital signal data; wherein processing the received stream ofdigital signal data through the one or more filters includes: processingthe received stream of digital signal data through a low-pass filter togenerate a second filtered stream of digital signal data, processing thereceived stream of digital signal data through an absolute value averagebaseline filter to determine an average of the absolute values of thereceived stream of digital signal data, and determining a differencebetween the second filtered stream of digital signal data and theaverage of the absolute values of the received stream of digital signaldata to generate a second filtered stream of digital signal data.
 4. Themethod of claim 3, further comprising: determining, from the continuallyreceived stream of digital signal data, that signal strength is abovethe threshold for a predefined amount of time; responsive to thedetermining that signal strength is above the threshold for thepredefined amount of time, performing the following: reenabling baselinecalibration for the stream of digital signal data; processing thereceived digital signal data using the machine learning model todetermine a third touch event and a third location on a display screenof the touchscreen; and processing the third touch event.
 5. The methodof claim 3, wherein the processing the received stream of digital signaldata through the low-pass filter and the processing the received streamof digital signal data through the absolute value average baselinefilter are performed concurrently.
 6. The method of claim 3, wherein thedisabling baseline calibration on the stream of digital signal dataincludes transmitting a command to an e-field controller to disablebaseline calibration.
 7. An electric field touchscreen, comprising: aset of one or more transmit electrodes that are configured to generatean electric field (e-field); a plurality of receive electrodes that areeach configured to sense an e-field variance; a controller configured tocontinuously receive signal data from the plurality of receiveelectrodes and output a stream of digital signal data that representse-field signal deviations; and an application processor configured toperform the following: receive the stream of digital signal data,process the received digital signal data using a machine learning modelto determine a first touch event and a first location on a displayscreen of the touchscreen, process the first touch event, determine,from the received stream of digital signal data, that signal strengthhas dropped below a threshold; and responsive to a determination thatsignal strength has dropped below the threshold, perform the following:process the received stream of digital signal data through one or morefilters to generate a first filtered stream of digital signal data,process the first filtered stream of digital signal data using themachine learning model to determine a second touch event and a secondlocation on the display screen of the touchscreen, and process thesecond touch event.
 8. The electric field touchscreen of claim 7,wherein the display screen includes four edges, and wherein theplurality of receive electrodes includes at least four receiveelectrodes located along the four edges of the display screenrespectively.
 9. The electric field touchscreen of claim 7, wherein themachine learning model is to use a deep neural network (DNN) regressionmodel to predict locations and is to use a DNN classifier model topredict whether touch events have occurred.
 10. The electric fieldtouchscreen of claim 7, wherein the application processor is furtherconfigured to perform the following: responsive to a determination thatthe signal strength has dropped below the threshold, further perform thefollowing: disable baseline calibration for the stream of digital signaldata, wherein to process the received stream of digital signal datathrough the one or more filters includes: process the received stream ofdigital signal data through a low-pass filter to generate a secondfiltered stream of digital signal data, process the received stream ofdigital signal data through an absolute value average baseline filter todetermine an average of the absolute values of the received stream ofdigital signal data; determine a difference between the second filteredstream of digital signal data and the average of the absolute values ofthe received stream of digital signal data to generate a second filteredstream of digital signal data.
 11. The electric field touchscreen ofclaim 10, wherein the application processor is further configured toperform the following: determine, from the received stream of digitalsignal data, whether that signal strength is above the threshold for apredefined amount of time; and responsive to a determination that signalstrength is above the threshold for the predefined amount of time,perform the following: reenable baseline calibration for the stream ofdigital signal data, process the received digital signal data using themachine learning model to determine a third touch event and a thirdlocation on a display screen of the touchscreen, and process the thirdtouch event.
 12. The electric field touchscreen of claim 10, whereinprocessing of the received stream of digital signal data through thelow-pass filter and processing of the received stream of digital signaldata through the absolute value average baseline filter are to beperformed concurrently.
 13. The electric field touchscreen of claim 10,wherein disablement of baseline calibration on the stream of digitalsignal data includes a command to be transmitted to the controller todisable baseline calibration.
 14. A non-transitory machine-readablestorage medium that provides instructions that, if executed by aprocessor of an electric field (e-field) touchscreen, will cause saidprocessor to perform operations comprising: continually receiving astream of digital signal data that represents e-field signal deviationsdetected from each of a plurality of receive electrodes, wherein adisplay screen of the e-field touchscreen includes four edges, andwherein the plurality of receive electrodes includes at least fourelectrodes located along the four edges of the display screenrespectively; processing the stream of digital signal data using amachine learning model to determine a first touch event and a firstlocation on a display screen of the touchscreen; processing the firsttouch event; determining, from the received stream of digital signaldata, that signal strength has dropped below a threshold; and responsiveto the determining that signal strength has dropped below the threshold,performing the following: processing the received stream of digitalsignal data through one or more filters to generate a first filteredstream of digital signal data, processing the first filtered stream ofdigital signal data using the machine learning model to determine asecond touch event and a second location on the display screen of thetouchscreen, and processing the second touch event.
 15. Thenon-transitory machine-readable storage medium of claim 14, wherein themachine learning model uses a deep neural network (DNN) regression modelto predict locations and uses a DNN classifier model to predict whethertouch events have occurred.
 16. The non-transitory machine-readablestorage medium of claim 14, wherein the operations further comprise:responsive to the determining that signal strength has dropped below thethreshold, further performing the following: disabling baselinecalibration for the stream of digital signal data; wherein processingthe received stream of digital signal data through the one or morefilters includes: processing the received stream of digital signal datathrough a low-pass filter to generate a second filtered stream ofdigital signal data, processing the received stream of digital signaldata through an absolute value average baseline filter to determine anaverage of the absolute values of the received stream of digital signaldata, and determining a difference between the second filtered stream ofdigital signal data and the average of the absolute values of thereceived stream of digital signal data to generate a second filteredstream of digital signal data.
 17. The non-transitory machine-readablestorage medium of claim 16, wherein the operations further comprise:determining, from the continually received stream of digital signaldata, that signal strength is above the threshold for a predefinedamount of time; responsive to the determining that signal strength isabove the threshold for the predefined amount of time, performing thefollowing: reenabling baseline calibration for the stream of digitalsignal data; processing the received digital signal data using themachine learning model to determine a third touch event and a thirdlocation on a display screen of the touchscreen; and processing thethird touch event.
 18. The non-transitory machine-readable storagemedium of claim 16, wherein the operations further comprise, wherein theprocessing the received stream of digital signal data through thelow-pass filter and the processing the received stream of digital signaldata through the absolute value average baseline filter are performedconcurrently.
 19. The non-transitory machine-readable storage medium ofclaim 16, wherein the operations further comprise, wherein the disablingbaseline calibration on the stream of digital signal data includestransmitting a command to an e-field controller to disable baselinecalibration.